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Semantic Similarity from Natural Language and Ontology Analysis (Paperback)
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Semantic Similarity from Natural Language and Ontology Analysis (Paperback)
Series: Synthesis Lectures on Human Language Technologies
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Artificial Intelligence federates numerous scientific fields in the
aim of developing machines able to assist human operators
performing complex treatments---most of which demand high cognitive
skills (e.g. learning or decision processes). Central to this quest
is to give machines the ability to estimate the likeness or
similarity between things in the way human beings estimate the
similarity between stimuli. In this context, this book focuses on
semantic measures: approaches designed for comparing semantic
entities such as units of language, e.g. words, sentences, or
concepts and instances defined into knowledge bases. The aim of
these measures is to assess the similarity or relatedness of such
semantic entities by taking into account their semantics, i.e.
their meaning---intuitively, the words tea and coffee, which both
refer to stimulating beverage, will be estimated to be more
semantically similar than the words toffee (confection) and coffee,
despite that the last pair has a higher syntactic similarity. The
two state-of-the-art approaches for estimating and quantifying
semantic similarities/relatedness of semantic entities are
presented in detail: the first one relies on corpora analysis and
is based on Natural Language Processing techniques and semantic
models while the second is based on more or less formal,
computer-readable and workable forms of knowledge such as semantic
networks, thesauri or ontologies. Semantic measures are widely used
today to compare units of language, concepts, instances or even
resources indexed by them (e.g., documents, genes). They are
central elements of a large variety of Natural Language Processing
applications and knowledge-based treatments, and have therefore
naturally been subject to intensive and interdisciplinary research
efforts during last decades. Beyond a simple inventory and
categorization of existing measures, the aim of this monograph is
to convey novices as well as researchers of these domains toward a
better understanding of semantic similarity estimation and more
generally semantic measures. To this end, we propose an in-depth
characterization of existing proposals by discussing their
features, the assumptions on which they are based and empirical
results regarding their performance in particular applications. By
answering these questions and by providing a detailed discussion on
the foundations of semantic measures, our aim is to give the reader
key knowledge required to: (i) select the more relevant methods
according to a particular usage context, (ii) understand the
challenges offered to this field of study, (iii) distinguish room
of improvements for state-of-the-art approaches and (iv) stimulate
creativity toward the development of new approaches. In this aim,
several definitions, theoretical and practical details, as well as
concrete applications are presented.
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